基于粒子滤波的汽车防撞雷达研究
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摘要
毫米波汽车防撞雷达是目前国内外正在研发的汽车安全系统的重要组成部分。由于工作于各种非高斯背景的干扰环境，利用以往传统的Kalman和扩展Kalman滤波等线性或近似线性的处理方法很难实现对目标的准确检测和跟踪。粒子滤波技术对于非线性系统和非高斯的干扰环境具有高度的适应性，可以很好解决以往算法精度不高甚至发散的问题，克服系统可靠性差的缺点。研究基于非线性和非高斯背景下的粒子滤波信号处理技术对于提高毫米波汽车防撞雷达性能，降低恶性交通事故的发生率，提高交通安全性有着极其重要的理论和现实意义。本文主要围绕基于粒子滤波技术的汽车防撞雷达开展工作，主要内容包括：
1．在介绍非线性滤波的基础上，引入粒子滤波，并通过实例对粒子滤波性能进行仿真，结果证明了在非线性系统非高斯背景下粒子滤波的优异性能；
2．简要介绍了前视车载雷达的预警功能、特性指标，分析了车载毫米波雷达系统的工作体制和天线的扫描方式，提出了采用混沌调频信号代替传统的线性调频信号的方案，通过仿真表明该信号在很大程度上可以减少由各种背景干扰及交叉干扰引起的虚警现象；
3．构造了整个车载雷达系统的原理框图，对其工作原理进行了讨论。通过分析和仿真证明了对单目标和多目标相关处理具有良好的测距性能；简单介绍了数字多波束技术和测角技术，仿真结果显示这种高分辨阵列测向技术具有高精度；
4．分析了粒子滤波在汽车防撞雷达系统中的应用，并与扩展卡尔曼滤波进行比较，仿真结果表明：粒子滤波很好地解决了扩展卡尔曼滤波跟踪精度不高甚至发散的问题，大大地提高了系统可靠性。
The millimeter-wave band automotive collision warning radar, which has been developing all over the world, is an important part of vehicle safety system. Because the radar is applied in all kinds of non-Gaussian interference circumstances, it is difficult to detect and track the target by using traditional Kalman and Extended Kalman methods which were liner or approximated linear measures. Particle filter is very fit for non-linear and non-Gaussian interference circumstances. It has a high-veracity. Such a vehicle radar used particle filter in practical transportation application can provide great benefits in safety and driver convenience. It has academic and practical significance. Research works and the major contributions in this thesis are as follows:
1. After discussing non-linear filter, particle filter is introduced. Both the theoretic analysis and simulation results show that the particle filter does well at a non-linear and non-Gaussian interference circumstances.
2. After analyzing the functions and specifications of the system, discussing radar working method and antenna scanning mode, the scheme using chaotic FM signal as a substitute of LFMCW signal is proposed, the results of analysis and simulation show that chaotic FM signal is superior to the traditional LFMCW signal in electromagnetic compatibility. It can solve the false alarm problem to some extent.
3. The principle block diagram of the vehicle radar system is given. The simulation results of correlation show that it has a good resolution of distance. The azimuth angle measurement problem of automotive collision warning radar is discussed. DBF is introduced. The simulation shows it has a good split-angle precision.
4. The application of particle filter in the millimeter-wave band automotive collision warning radar is analyzed, the simulation show that the performance of particle filter is superior to extend Kalman, it can solve the problem of low- precision and low-reliability.
1．在介绍非线性滤波的基础上，引入粒子滤波，并通过实例对粒子滤波性能进行仿真，结果证明了在非线性系统非高斯背景下粒子滤波的优异性能；
2．简要介绍了前视车载雷达的预警功能、特性指标，分析了车载毫米波雷达系统的工作体制和天线的扫描方式，提出了采用混沌调频信号代替传统的线性调频信号的方案，通过仿真表明该信号在很大程度上可以减少由各种背景干扰及交叉干扰引起的虚警现象；
3．构造了整个车载雷达系统的原理框图，对其工作原理进行了讨论。通过分析和仿真证明了对单目标和多目标相关处理具有良好的测距性能；简单介绍了数字多波束技术和测角技术，仿真结果显示这种高分辨阵列测向技术具有高精度；
4．分析了粒子滤波在汽车防撞雷达系统中的应用，并与扩展卡尔曼滤波进行比较，仿真结果表明：粒子滤波很好地解决了扩展卡尔曼滤波跟踪精度不高甚至发散的问题，大大地提高了系统可靠性。
The millimeter-wave band automotive collision warning radar, which has been developing all over the world, is an important part of vehicle safety system. Because the radar is applied in all kinds of non-Gaussian interference circumstances, it is difficult to detect and track the target by using traditional Kalman and Extended Kalman methods which were liner or approximated linear measures. Particle filter is very fit for non-linear and non-Gaussian interference circumstances. It has a high-veracity. Such a vehicle radar used particle filter in practical transportation application can provide great benefits in safety and driver convenience. It has academic and practical significance. Research works and the major contributions in this thesis are as follows:
1. After discussing non-linear filter, particle filter is introduced. Both the theoretic analysis and simulation results show that the particle filter does well at a non-linear and non-Gaussian interference circumstances.
2. After analyzing the functions and specifications of the system, discussing radar working method and antenna scanning mode, the scheme using chaotic FM signal as a substitute of LFMCW signal is proposed, the results of analysis and simulation show that chaotic FM signal is superior to the traditional LFMCW signal in electromagnetic compatibility. It can solve the false alarm problem to some extent.
3. The principle block diagram of the vehicle radar system is given. The simulation results of correlation show that it has a good resolution of distance. The azimuth angle measurement problem of automotive collision warning radar is discussed. DBF is introduced. The simulation shows it has a good split-angle precision.
4. The application of particle filter in the millimeter-wave band automotive collision warning radar is analyzed, the simulation show that the performance of particle filter is superior to extend Kalman, it can solve the problem of low- precision and low-reliability.
引文
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22. Simon Godsill, Tim Clapp. Improvement Strategies for Monte Carlo Particle Filters.Signal Processing Group, University of Cambridge
23. S. Arulampalam and B. Ristic. Comparison of the particle filter with range parameterized and modified polar EKF's for angle-only tracking. Proc. SPIE, vol.4048.2000: pp. 288-299,
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2．周晋．20世纪汽车驾驶智能化成果纵览汽车与安全．No4-1999：1919．12-13
3．周杰．超越巡航控制．国外技术动态．No3-2002：1pp22-24
4．李晓霞，江宗法，李百川，边浩毅．车载距离探测技术比较．长安大学学报(自然科学版)．Vol．22 No．2．Mar．2003：19073-76
5．吕立波．汽车避撞技术及其发展．国外技术动态．2002．6：pp．20-22
6．吴雄．汽车防撞毫米波雷达研制现状．电子与仪表．No6-1997：pp．38-40
7．张建辉，黄茂三．汽车防撞雷达概述．World Automobile，No6-2000，1919．16-19
8．程晓佩．汽车雷达的应用与发展前景．国内外雷达动态
9．瞿墨．让汽车聪明起来．汽车市场．No6-2002：pp．47
10. Holger H. Meinel. Applications of Microwaves and Millimeterwaves for Vehicle Communications and Control in Europe. IEEE MTT-S, 1996
11. M.Sanjeev Arulampalam, Simon Maskell, Neil Gordon, Tim Clapp. A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Transactions on Signal Processing. Vol.50, NO. 2. February 2002:pp.174-188
12. Arnaud Doucet, Simon Godsill, Christophe Andrieu. On Sequential Monte Carlo Sampling Methods for Bayesian Filtering. Signal Processing Group, Department of Engineering University of Cambridge
13. Si-Eun Lee, Byoung-Tak Zhang, Arnaud Doucet. Convergence Properties of Bayesian Evolutionary Algorithms with Population Size Greater Than 1.0-7803-6657-3/01,2001 IEEE
14. Jonathan Deutscher, Andrew Blake, Ian Reid. Articulated Body Motion Capture by Annealed Particle Filtering. 1063-6919/00, 2000 IEEE
15. Y.Boers. on the Number of Samples to Be Drawn in Particle Filtering. Hollandse Signaalapparaten B.V. Zuidelijike Havenweg 40, 7554RR Hengelo, The Netherlands,Research Development and Technology Functional Design
16. William Fong, Simon Godsill, Arnaud Doueet, Mike West. Monte Carlo Smoothing with Application to Audio Signal Enhancement
17. Christian Musso, Nadia Oudjane. Particle Methods For Multimodal Filtering Application To Terrain Navigation. ONERA DTM. BP 7292322, France
18. Jayesh Hukumchand Kotecha. Sequential Monte Carla Methods for Dynamic State Space Models with Applications to Communications. Electrical Engineering State University of New York at Stony Brook. 2001
19. Per-Johan Nordlund, Fredrik Gustafsson.Recursive Estimation of Three-Dimensional Aircraft Position Using Terrain-aided Positioning.0-7803-7402-9/02 2002 IEEE:pp. 1121-1124
20. Jayesh H.Kotecha, Petar M.Djuri(?). Sequential Detector for Nonlinear Channels with Applications to Satellite Communications. 0-7803-7402-9/02, 2002 IEEE:pp.2465-2468
21. Erik B(?)lviken, Geir Storvik. Deterministic and Stochastic Particle Filters in State Space Models. University of Oslo, The Norwegian Computing Center
22. Simon Godsill, Tim Clapp. Improvement Strategies for Monte Carlo Particle Filters.Signal Processing Group, University of Cambridge
23. S. Arulampalam and B. Ristic. Comparison of the particle filter with range parameterized and modified polar EKF's for angle-only tracking. Proc. SPIE, vol.4048.2000: pp. 288-299,
24. N. Bergman. Recursive Bayesian Estimation: Navigation and tracking applications.Ph.D. dissertation, Linkrping Univ., Linkrping, Sweden. 1999
25. N. Bergman, A. Doucet, and N. Gordon. Optimal estimation and Cramer-Rao bounds for partial non-Gaussian state space models. Ann. Inst. Statist. Math., vol.53, no. 1. 2001 pp. 97-112
26. B. P. Carlin, N. G. Polson, and D. S. Stoffer. A Monte Carlo approach to nonnormal and nonlinear state-space modeling. J. Amer. Statist. Assoc., vol. 87, no. 418. 1992:pp. 493-500
27. J. Carpenter, P. Clifford, and P. Feamhead. Improved particle filter for nonlinear problems. Proc. Inst. Elect. Eng., Radar, Sonar, Navig., 1999
28. P. Del Moral. Measure valued processes and interacting particle systems.Application to nonlinear filtering problems. Ann. Appl. Probab., vol. 8, no. 2. 1998:pp. 438-495
29. D. Crisan, P. Del Moral, and T. J. Lyons. Non-linear filtering using branching and interacting particle systems. Markov Processes Related Fields, vol. 5, no. 3. 1999:pp. 293-319
30. Jason J.Ford. Non-linear and Robust Filtering: From the Kalman Filter to the
Particle Filter. DSTO Aeronautical and Maritime Research Laboratory, April, 2002
31. Dan Crisan, Amaud Doucet. A Survey of Convergence Results on Particle Filtering Methods for Practitioners, IEEE Transactions on Signal Processing.VOL.50,NO.3,March 2002:pp736-746
32．李庆扬，关治，白峰杉．数值计算原理．清华大学出版社．2000
33．盛骤，谢式千，潘承毅．概率论与数理统计．高等教育出版社．1989
34. Steven M.Kay. Fundamentals of Statistic Signal Processing: Estimation and Detection Theory. Publishing House of Electronics Industry. 2003
35．徐钟济．蒙特卡罗方法．上海科学技术出版社． 1985
36．裴鹿成，王仲奇．蒙特卡罗方法及其应用．北京-海洋出版社．1998
37．裴鹿成，张孝泽．蒙特卡罗方法及其在粒子输运问题中的应用．北京．科学出版社．1980
38．丁鹭飞，耿富录．雷达原理．西安电子科技大学出版社．1995．6
39．孙仲康．雷达数据数字处理．北京．国防工业出版社．1983
40．(意)法里纳(Fanna,A．)，(意)施图德(Studer,F.A．)．雷达数据处理．北京．国防工业出版社．1988．2
41．张贤达，保铮．通信信号处理．北京．国防工业出版社，2000
42．王宏禹．现代谱估计．南京．东南大学出版社．1990
43．周宏仁，敬忠良，王培德．机动目标跟踪．北京-国防工业出版社．1991．8
44. Eric Shih-Syou Li. Millimeter-wave polarimetric Radar system as an advanced vehicle control and warning sensor. A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Electrical Engineering in The University of Michigan. 1998
45. Jean-Ren(?) Larocque, James P. Reilly, William Ng. Particle Filters for Tracking an Unknown Number of Sources. IEEE Transactions on Signal Processing. May, 2001
46. E. Bolviken, P. J. Acklam, N. Christophersen, and J. M. Stordal. Monte Carlo filters for non-linear state estimation. Automatics, Vol. 37. pp. 177-183, 2001
47. Shawn Herman, Pierre Moulin. A Particle Filtering Approach to FM-Band Passive Radar Tracking and Automatic Target Recognition. 0-7803-7231-X/01,IEEE.2002.pp: 1789-1808
48. Craig S. Agate. A Particle Filtering Algorithm for Tracking and Identifying Multiple Ground Targets. Toyon Research Corporation. 2003
49. Eric Shih-Syou Li. Millimeter-wave Polarimetric Radar System as an Advanced
Vehicle Control and Warning Sensor. The University of Michigan. 1998
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